| dc.description.abstract | Nitrogen (N) fertilizers are essential for sustaining the productivity of winter wheat in
humid agroecosystems; yet their management poses a significant dual challenge: optimizing farm
profitability while reducing environmental impact. In the southeastern U.S., heavy winter rainfall
often exceeds evapotranspiration, resulting in substantial N losses through leaching and
denitrification, which degrade water quality and reduce N use efficiency (NUE). Although the
widespread adoption of yield zones, the fate and flow of N within these contrasting zones remain
inadequately measured, particularly under long-term climatic variability. The goal of this study
was to systematically quantify N dynamics and environmental losses (Nenvloss) across contrasting
high-yielding (HYZ) and low-yielding (LYZ) zones and subsequently develop integrated, sitespecific management strategies to balance conflicting economic and environmental objectives.
Due to the difficulty in the direct measurement of subsurface fluxes, this study utilized the DSSAT
CSM-CERES-Wheat model to simulate crop-soil-weather interactions and associated N dynamics
across contrasting yield zones. The model calibration (2022-23) and evaluation (2023-24) using
two distinct seasons of field-observed data validated the model’s reliability in simulating winter
wheat growth, yield, and soil N balance. The simulations identified nitrate (NO3) leaching as the
dominant loss pathway, accounting for 94-98% of total losses. A distinct yield zone pattern
emerged, where HYZ demonstrated higher leaching potential (39-98 kg[N] ha-1) compared to the
LYZ (69-70 kg[N] ha-1). This was primarily driven by higher initial soil mineral N (Nmineral) and
enhanced soil permeability rather than differences in crop N uptake. Moreover, weather variability
significantly influenced these dynamics, with leaching losses increasing by 48% in wet years
compared to drought years, highlighting the inadequacy of static nutrient management
recommendations in humid regions. This study further explored integrated strategies to address
the trade-offs between yield maximization and environmental sustainability. A systems analysis
was conducted to optimize both planting dates and N management strategies by integrating the
CSM-CERES-Wheat model with a Multi-Criteria Decision Analysis (MCDA) framework over 32
years. The results indicated that optimizing the planting window to mid-October significantly
increased yield (21-28%) and reduced Nenvloss (14-19%) compared to late planting. The hybrid
Analytical Hierarchy Process-Test for Order of Preference by Similarity to Ideal Solution (AHPTOPSIS) analysis revealed that the farmer’s baseline strategy (T1: 130 kg[N] ha-1 UAN + 5 Mg
ha-1 PL-Fall) was economically inefficient despite a higher yield (3.7-3.8 Mg ha-1). The optimal
nutrient management strategies varied by yield zone. In the high‑yield zone (HYZ), profitability
was maximized with a split application of UAN at 130 kg N ha⁻¹ (T4). In contrast, the low‑yield
zone (LYZ) benefited most from a 25% reduction in UAN (to 130 kg N ha⁻¹) combined with
fall‑applied poultry litter at 5 Mg ha⁻¹ (T6). This integrated approach increased profitability by 36–
70% (equivalent to $68–$146 ha⁻¹) while simultaneously reducing environmental nitrogen losses
(Nenvloss) by 4–8 kg N ha⁻¹. Overall, this comprehensive study emphasized the critical importance
of precision agriculture, indicating that Nenvloss in winter wheat was primarily influenced by soil
hydrological properties and climatic variability rather than yield potential. These findings support
a paradigm shift from uniform, yield-based inputs to weather-responsive, site-specific strategies,
providing a robust framework for enhancing NUE, sustaining high productivity, and minimizing
the environmental impact of agricultural systems in high-rainfall regions.
Artificial Intelligence (AI) Use Disclosure Statement
In the preparation of this thesis/dissertation, the following Artificial Intelligence (AI) tools
were used: Grammarly and Gemini. These tools were used primarily to assist with grammatical
editing and support in coding for data analysis. The author acknowledges full responsibility for
the intellectual content of this work and has ensured that all AI-assisted sections have been
reviewed and revised for accuracy and appropriate academic style. All AI-generated content was
reviewed and validated for relevance, appropriateness, and accuracy before incorporation into the
final document to maintain the scholarly integrity of this research. | en_US |
